MCPInstall

research-refine-pipeline

Run an end-to-end workflow that chains research-refine and experiment-plan. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill research-refine-pipeline
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads research-refine-pipeline automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Research Refine Pipeline: End-to-End Method and Experiment Planning

Before each reviewer slot, follow Selectable Reviewers: resolve invocation → .aris/reviewer.json → Claude Code default (Codex MCP) with reviewer_route.py through the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only to backend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections are REVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.

Refine and concretize: $ARGUMENTS

Overview

Use this skill when the user does not want to stop at a refined method. The goal is to produce a coherent package that includes:

  • a problem-anchored, elegant final proposal
  • the review history explaining why the method is focused
  • a detailed experiment roadmap tied to the paper's claims
  • a compact pipeline summary that says what to run next

This skill composes two existing workflows:

  1. research-refine for method refinement
  2. experiment-plan for claim-driven validation planning

For stage-specific detail, read these sibling skills only when needed:

  • ../research-refine/SKILL.md
  • ../experiment-plan/SKILL.md

Core Rule

Do not plan a large experiment suite on top of an unstable method. First stabilize the thesis. Then turn the stable thesis into experiments.

Default Outputs

  • refine-logs/FINAL_PROPOSAL.md
  • refine-logs/REVIEW_SUMMARY.md
  • refine-logs/REFINEMENT_REPORT.md
  • refine-logs/EXPERIMENT_PLAN.md
  • refine-logs/EXPERIMENT_TRACKER.md
  • refine-logs/PIPELINE_SUMMARY.md

Workflow

Phase 0: Triage the Starting Point

  • Extract the problem, rough approach, constraints, resources, and target venue.
  • Check whether refine-logs/FINAL_PROPOSAL.md already exists and still matches the current request.
  • If the proposal is missing, stale, or materially different from the current request, run the full research-refine stage.
  • If the proposal is already strong and aligned, reuse it and jump to experiment planning.
  • If in doubt, prefer re-running research-refine rather than planning experiments for the wrong method.

Phase 1: Method Refinement Stage

Run the research-refine workflow and keep its V3 philosophy intact:

  • preserve the Problem Anchor
  • prefer the smallest adequate mechanism
  • keep one dominant contribution
  • modernize only when it improves the paper

Exit this stage only when these are explicit:

  • the final method thesis
  • the dominant contribution
  • the complexity intentionally rejected
  • the key claims and must-run ablations
  • the remaining risks, if any

If the verdict is still REVISE, continue into experiment planning only if the remaining weaknesses are clearly documented.

Phase 2: Planning Gate

Before the experiment stage, write a short gate check:

  • What is the final method thesis?
  • What is the dominant contribution?
  • What complexity was intentionally rejected?
  • Which reviewer concerns still matter for validation?
  • Is a frontier primitive central, optional, or absent?

If these answers are not crisp, tighten the final proposal first.

Phase 3: Experiment Planning Stage

Run the experiment-plan workflow grounded in:

  • refine-logs/FINAL_PROPOSAL.md
  • refine-logs/REVIEW_SUMMARY.md
  • refine-logs/REFINEMENT_REPORT.md

Ensure the experiment plan covers:

  • the main anchor result
  • novelty isolation
  • a simplicity or deletion check
  • a frontier necessity check if applicable
  • run order, budget, and decision gates

Phase 4: Integration Summary

Write refine-logs/PIPELINE_SUMMARY.md:

# Pipeline Summary

**Problem**: [problem]
**Final Method Thesis**: [one sentence]
**Final Verdict**: [READY / REVISE / RETHINK]
**Date**: [today]

## Final Deliverables
- Proposal: `refine-logs/FINAL_PROPOSAL.md`
- Review summary: `refine-logs/REVIEW_SUMMARY.md`
- Experiment plan: `refine-logs/EXPERIMENT_PLAN.md`
- Experiment tracker: `refine-logs/EXPERIMENT_TRACKER.md`

## Contribution Snapshot
- Dominant contribution:
- Optional supporting contribution:
- Explicitly rejected complexity:

## Must-Prove Claims
- [Claim 1]
- [Claim 2]

## First Runs to Launch
1. [Run]
2. [Run]
3. [Run]

## Main Risks
- [Risk]:
- [Mitigation]:

## Next Action
- Proceed to `/run-experiment`

Phase 5: Present a Brief Summary to the User

Pipeline complete.

Method output:
- refine-logs/FINAL_PROPOSAL.md

Experiment output:
- refine-logs/EXPERIMENT_PLAN.md
- refine-logs/EXPERIMENT_TRACKER.md

Pipeline summary:
- refine-logs/PIPELINE_SUMMARY.md

Best next step:
- /run-experiment

Output Protocols

Follow these shared protocols for all output files:

  • Output Versioning Protocol — write timestamped file first, then copy to fixed name
  • Output Manifest Protocol — log every output to MANIFEST.md
  • Output Language Protocol — respect the project's language setting

Key Rules

  • Large file handling: If the Write tool fails due to file size, immediately retry using Bash (cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.

  • Do not let the experiment plan override the Problem Anchor.

  • Do not widen the paper story after method refinement unless a missing validation block is truly necessary.

  • Reuse the same claims across FINAL_PROPOSAL.md, EXPERIMENT_PLAN.md, and PIPELINE_SUMMARY.md.

  • Keep the main paper story compact.

  • If the method is intentionally simple, defend that simplicity in the experiment plan rather than adding new components.

  • If the method uses a modern LLM / VLM / Diffusion / RL primitive, make its necessity test explicit.

  • If the method does not need a frontier primitive, say that clearly and avoid forcing one.

  • Prefer the staged skills when the user only needs one stage; use this skill for the integrated flow.

Composing with Other Skills

/research-refine-pipeline -> one-shot method + experiment planning
/research-refine   -> method refinement only
/experiment-plan   -> experiment planning only
/run-experiment    -> execution

FAQ

What does the research-refine-pipeline skill do?

Run an end-to-end workflow that chains research-refine and experiment-plan. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.

How do I install research-refine-pipeline?

Run npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill research-refine-pipeline in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/research-refine-pipeline/ (personal) or .claude/skills/research-refine-pipeline/ (project).

Which AI agents can use research-refine-pipeline?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is research-refine-pipeline safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains research-refine-pipeline?

It is published by wanshuiyin in wanshuiyin/auto-claude-code-research-in-sleep. It has 508 recorded installs.

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